Editing Numerical weather prediction (section)

== Applications ==

===Air quality modeling===
{{see also|Atmospheric dispersion modeling}}
[[Air pollution forecasting|Air quality forecasting]] attempts to predict when the concentrations of pollutants will attain levels that are hazardous to public health. The concentration of pollutants in the atmosphere is determined by their ''transport'', or [[Arithmetic mean|mean]] velocity of movement through the atmosphere, their [[diffusion]], [[chemical transformation]], and ground [[Deposition (aerosol physics)|deposition]].<ref>{{cite book|url=http://www.envirocomp.org/books/chapters/2aap.pdf|page=16|title=Ambient Air Pollution|author1=Daly, Aaron  |author2=Paolo Zannetti |name-list-style=amp |publisher=The Arab School for Science and Technology and The EnviroComp Institute|year=2007|access-date=2011-02-24}}</ref> In addition to pollutant source and terrain information, these models require data about the state of the [[fluid flow]] in the atmosphere to determine its transport and diffusion.<ref name="Baklanov">{{cite journal|last=Baklanov|first=Alexander|author2=Rasmussen, Alix |author3=Fay, Barbara |author4=Berge, Erik |author5= Finardi, Sandro  |title=Potential and Shortcomings of Numerical Weather Prediction Models in Providing Meteorological Data for Urban Air Pollution Forecasting|journal=Water, Air, & Soil Pollution: Focus|date=September 2002|volume=2|issue=5|pages=43–60|doi=10.1023/A:1021394126149|s2cid=94747027}}</ref> Meteorological conditions such as [[thermal inversion]]s can prevent surface air from rising, trapping pollutants near the surface,<ref>{{cite book|last=Marshall|first=John|title=Atmosphere, ocean, and climate dynamics: an introductory text|url=https://archive.org/details/atmosphereoceanc00mars|url-access=limited|year=2008|publisher=Elsevier Academic Press|location=Amsterdam|isbn=978-0-12-558691-7|pages=[https://archive.org/details/atmosphereoceanc00mars/page/n64 44]–46|author2=Plumb, R. Alan }}</ref> which makes accurate forecasts of such events crucial for air quality modeling. Urban air quality models require a very fine computational mesh, requiring the use of high-resolution mesoscale weather models; in spite of this, the quality of numerical weather guidance is the main uncertainty in air quality forecasts.<ref name="Baklanov"/>

===Climate modeling===
{{See also|Global climate model}}
A General Circulation Model (GCM) is a [[mathematical model]] that can be used in computer simulations of the global circulation of a planetary [[atmosphere]] or ocean. An atmospheric general circulation model (AGCM) is essentially the same as a global numerical weather prediction model, and some (such as the one used in the UK Unified Model) can be configured for both short-term weather forecasts and longer-term climate predictions. Along with [[sea ice]] and land-surface components, AGCMs and oceanic GCMs (OGCM) are key components of global climate models, and are widely applied for understanding the [[climate]] and projecting [[climate change]]. For aspects of climate change, a range of man-made chemical emission scenarios can be fed into the climate models to see how an enhanced [[greenhouse effect]] would modify the Earth's climate.<ref>{{cite book|url=https://books.google.com/books?id=Pzx_Nz1qgd8C&pg=PA40|author=Australian Bureau of Statistics|title=Year book, Australia, Issue 87|page=40|year=2005|access-date=2011-02-18}}</ref> Versions designed for climate applications with time scales of decades to centuries were originally created in 1969 by [[Syukuro Manabe]] and [[Kirk Bryan (oceanographer)|Kirk Bryan]] at the [[Geophysical Fluid Dynamics Laboratory]] in [[Princeton, New Jersey]].<ref>{{cite web |url=http://celebrating200years.noaa.gov/breakthroughs/climate_model/welcome.html |title=The First Climate Model |author=[[National Oceanic and Atmospheric Administration]] 200th Celebration |publisher=[[National Oceanic and Atmospheric Administration]]|date=2008-05-22 |access-date=2010-04-20 }}</ref> When run for multiple decades, computational limitations mean that the models must use a coarse grid that leaves smaller-scale interactions unresolved.<ref>{{cite book|url=https://books.google.com/books?id=bV3C5VCC-0EC&pg=PA282|pages=284–289|title=The global climate system: patterns, processes, and teleconnections|author=Bridgman, Howard A., John E. Oliver, Michael H. Glantz|year=2006|publisher=Cambridge University Press|isbn=978-0-521-82642-6|access-date=2011-02-18}}</ref>

===Ocean surface modeling===
[[File:NOAA Wavewatch III Sample Forecast.gif|right|thumb|280px|NOAA Wavewatch III 120-hour wind and wave forecast for the North Atlantic|alt=A wind and wave forecast for the North Atlantic Ocean. Two areas of high waves are identified: One west of the southern tip of Greenland, and the other in the North Sea. Calm seas are forecast for the Gulf of Mexico. Wind barbs show the expected wind strengths and directions at regularly spaced intervals over the North Atlantic.]]
{{main|Marine weather forecasting|Ocean dynamics|Wind wave model}}
The transfer of energy between the wind blowing over the surface of an ocean and the ocean's upper layer is an important element in wave dynamics.<ref>{{cite journal|last=Chalikov|first=D. V.|title=The numerical simulation of wind-wave interaction|journal=[[Journal of Fluid Mechanics]]|date=August 1978|volume=87|issue=3|pages=561–82|doi=10.1017/S0022112078001767|bibcode=1978JFM....87..561C|s2cid=122742282 }}</ref> The [[spectral wave transport equation]] is used to describe the change in wave spectrum over changing topography. It simulates wave generation, wave movement (propagation within a fluid), [[wave shoaling]], [[refraction]], energy transfer between waves, and wave dissipation.<ref>{{cite book|page=270|url=https://books.google.com/books?id=yBtOwfUG6cgC|title=Numerical modeling of water waves|author=Lin, Pengzhi|publisher=Psychology Press|year=2008|isbn=978-0-415-41578-1}}</ref> Since surface winds are the primary forcing mechanism in the spectral wave transport equation, ocean wave models use information produced by numerical weather prediction models as inputs to determine how much energy is transferred from the atmosphere into the layer at the surface of the ocean. Along with dissipation of energy through [[Wind wave|whitecaps]] and [[resonance]] between waves, surface winds from numerical weather models allow for more accurate predictions of the state of the sea surface.<ref>{{cite journal|last=Bender|first=Leslie C.|title=Modification of the Physics and Numerics in a Third-Generation Ocean Wave Model|journal=[[Journal of Atmospheric and Oceanic Technology]]|date=January 1996|volume=13|issue=3|pages=726–750 |doi=10.1175/1520-0426(1996)013<0726:MOTPAN>2.0.CO;2|bibcode=1996JAtOT..13..726B|doi-access=free}}</ref>

===Tropical cyclone forecasting===
{{see also|Tropical cyclone forecast model}}
Tropical cyclone forecasting also relies on data provided by numerical weather models. Three main classes of [[Tropical cyclone forecast model|tropical cyclone guidance models]] exist: Statistical models are based on an analysis of storm behavior using climatology, and correlate a storm's position and date to produce a forecast that is not based on the physics of the atmosphere at the time. Dynamical models are numerical models that solve the governing equations of fluid flow in the atmosphere; they are based on the same principles as other limited-area numerical weather prediction models but may include special computational techniques such as refined spatial domains that move along with the cyclone. Models that use elements of both approaches are called statistical-dynamical models.<ref>{{cite web|title=Technical Summary of the National Hurricane Center Track and Intensity Models|url=http://www.nhc.noaa.gov/pdf/model_summary_20090724.pdf|publisher=National Oceanic and Atmospheric Administration|access-date=2011-02-19|author=[[National Hurricane Center]]|date=July 2009}}</ref>

In 1978, the first [[tropical cyclone forecast model|hurricane-tracking model]] based on [[Atmospheric dynamics#Dynamic meteorology|atmospheric dynamics]]—the movable fine-mesh (MFM) model—began operating.<ref name="Shuman W&F"/> Within the field of [[tropical cyclone track forecasting]], despite the ever-improving dynamical model guidance which occurred with increased computational power, it was not until the 1980s when numerical weather prediction showed [[Forecast skill|skill]], and until the 1990s when it consistently outperformed [[statistical model|statistical]] or simple dynamical models.<ref>{{cite web|url=http://www.nhc.noaa.gov/verification/verify6.shtml|publisher=[[National Hurricane Center]]|date=2010-04-20|access-date=2011-01-02|author=Franklin, James|title=National Hurricane Center Forecast Verification|author-link=James Franklin (meteorologist)}}</ref> Predictions of the intensity of a tropical cyclone based on numerical weather prediction continue to be a challenge, since statistical methods continue to show higher skill over dynamical guidance.<ref>{{cite journal|author=Rappaport, Edward N. |author2=Franklin, James L. |author3=Avila, Lixion A. |author4=Baig, Stephen R. |author5=Beven II, John L. |author6=Blake, Eric S. |author7=Burr, Christopher A. |author8=Jiing, Jiann-Gwo |author9=Juckins, Christopher A. |author10=Knabb, Richard D. |author11=Landsea, Christopher W. |author12=Mainelli, Michelle |author13=Mayfield, Max |author14=McAdie, Colin J. |author15=Pasch, Richard J. |author16=Sisko, Christopher |author17=Stewart, Stacy R. |author18=Tribble, Ahsha N.|title=Advances and Challenges at the National Hurricane Center|journal=[[Weather and Forecasting]]|date=April 2009|volume=24|issue=2|pages=395–419|doi=10.1175/2008WAF2222128.1|bibcode=2009WtFor..24..395R|citeseerx=10.1.1.207.4667 |s2cid=14845745 }}</ref>

===Weather forecasts===

Because weather drifts across the world, producing forecasts a week or more in advance typically involves running a numerical prediction model for the entire planet. Agencies use various software to do this, including:
* [[North American Ensemble Forecast System]], which combines results from:
** [[Global Forecast System]] from the US [[National Weather Service]]
** [[Global Environmental Multiscale Model]] from the [[Canadian Meteorological Centre]]
* [[Integrated Forecast System]] from the [[European Centre for Medium-Range Weather Forecasts]] and [[Météo-France]]
* [[Unified Model]], produced by a partnership of:
** UK [[Met Office]]
** Australia [[Bureau of Meteorology]]
** (South) [[Korea Meteorological Administration]]
** India [[National Centre for Medium Range Weather Forecasting]]<ref>[https://ncmrwf.gov.in/ncmrwf/NCUMG-Writeup-for-WEB-June2020.pdf Global NCMRWF Unified Model (NCUM-G) System]</ref>
** New Zealand [[National Institute of Water and Atmospheric Research]]
* [[Icosahedral Nonhydrostatic]] (ICON) from [[Deutscher Wetterdienst]], the German Meteorological Service
* [[Navy Global Environmental Model]] from the US Navy [[Fleet Numerical Meteorology and Oceanography Center]]
* Global Spectral Model and Global Ensemble Prediction System from the [[Japan Meteorological Agency]]<ref name="JMA">[https://www.jma.go.jp/jma/en/Activities/nwp.html Numerical Weather Prediction Activities]</ref>
* [[China Meteorological Administration]] Global Assimilation Forecasting System<ref name="CMA">[https://www.cma.gov.cn/en/forecast/highlight/202311/t20231117_5892086.html Numerical Weather Prediction]</ref>
* Brazilian Global Atmospheric Model (BAM) from [[Centro de Previsão do Tempo e Estudos Climáticos]] (CPTEC)

The global models can be used to supply [[boundary conditions]] to higher-resolution models that provide more accurate forecasts for an area of interest, such as the country served by a government agency, or an area where military action or rescue efforts are planned.
* Users of the Unified Model re-run the same system (hence the name) for a specific country or crisis zone at a higher horizontal resolution, feeding it the output of the global Unified Model run. This is given a different name, such as the UKV model or the New Zealand Limited Area Model.<ref>[https://www.nesi.org.nz/case-studies/improving-new-zealands-weather-forecasting-ability A 36 hour forecast by NZCSM takes 130 minutes to complete using 810 processors spread across 13 nodes of FitzRoy]</ref>
* The US National Weather Service runs the [[Weather Research and Forecasting Model]] with different parameters to create:
** [[North American Mesoscale Model]] (NAM) every six hours (with an ensemble called Short Range Ensemble Forecast, SREF)
** [[Rapid Refresh (weather prediction)|Rapid Refresh]] (RAP) and High Resolution Rapid Refresh (HRRR), every hour<ref>[https://rapidrefresh.noaa.gov/ Rapid Refresh (RAP)]</ref><ref>[https://rapidrefresh.noaa.gov/hrrr/ The High-Resolution Rapid Refresh (HRRR)]</ref>
* The Japan Meteorological Agency runs:<ref name="JMA" />
** Meso-Scale Model (MSM) every 3 hours, looking 39 and 78 hours ahead
** Meso-scale Ensemble Prediction System every 6 hours, looking 39 hours ahead (providing uncertainty estimation)
** Local Forecast Model every hour, looking 10-18 hours ahead
* The China Meteorological Administration runs the Regional Numerical Forecasting Model (CMA-MESO)<ref name="CMA" />
* CPTEC runs the Brazilian Regional Atmospheric Modelling System (BRAMS) and ETA Regional Model (ETA) for South America

The output of higher-resolution models may be further modified by [[model output statistics]] to take into quirky local phenomena that general models do not handle well, such as [[mountain waves]].

===Wildfire modeling===
{{Main|Wildfire modeling}}
[[File:Propagation model wildfire (English).svg|thumb|280px|right|A simple wildfire propagation model]]
On a molecular scale, there are two main competing reaction processes involved in the degradation of [[cellulose]], or wood fuels, in [[wildfire]]s. When there is a low amount of moisture in a cellulose fiber, [[volatilization]] of the fuel occurs; this process will generate intermediate gaseous products that will ultimately be the source of [[combustion]]. When moisture is present—or when enough heat is being carried away from the fiber, [[charring]] occurs. The [[chemical kinetics]] of both reactions indicate that there is a point at which the level of moisture is low enough—and/or heating rates high enough—for combustion processes to become self-sufficient. Consequently, changes in wind speed, direction, moisture, temperature, or [[lapse rate]] at different levels of the atmosphere can have a significant impact on the behavior and growth of a wildfire. Since the wildfire acts as a heat source to the atmospheric flow, the wildfire can modify local [[advection]] patterns, introducing a [[Feedback|feedback loop]] between the fire and the atmosphere.<ref name="Sullivan wildfire">{{cite journal|last=Sullivan|first=Andrew L.|title=Wildland surface fire spread modelling, 1990–2007. 1: Physical and quasi-physical models|journal=International Journal of Wildland Fire|date=June 2009|volume=18|issue=4|page=349|doi=10.1071/WF06143|arxiv=0706.3074|s2cid=16173400}}</ref>

A simplified two-dimensional model for the spread of wildfires that used [[convection]] to represent the effects of wind and terrain, as well as [[Thermal radiation|radiative heat transfer]] as the dominant method of heat transport led to [[reaction–diffusion system]]s of [[partial differential equation]]s.<ref name="Asensio-2002-WFM">{{cite journal|author1=Asensio, M. I.   |author2=L. Ferragut |name-list-style=amp |title=On a wildland fire model with radiation|journal=International Journal for Numerical Methods in Engineering|volume=54|issue=1 |pages=137–157|year=2002|doi=10.1002/nme.420|bibcode = 2002IJNME..54..137A |s2cid=122302719 }}</ref><ref name="Mandel-2008-WMD">{{cite journal|author=Mandel, Jan, [[Lynn Schreyer|Lynn S. Bennethum]], Jonathan D. Beezley, [[Janice Coen|Janice L. Coen]], Craig C. Douglas, Minjeong Kim, and Anthony Vodacek|title=A wildfire model with data assimilation|journal=Mathematics and Computers in Simulation|volume=79|pages=584–606|year=2008|doi=10.1016/j.matcom.2008.03.015|arxiv=0709.0086|bibcode=2007arXiv0709.0086M|issue=3|s2cid=839881}}</ref> More complex models join numerical weather models or [[computational fluid dynamics]] models with a wildfire component which allow the feedback effects between the fire and the atmosphere to be estimated.<ref name="Sullivan wildfire"/> The additional complexity in the latter class of models translates to a corresponding increase in their computer power requirements. In fact, a full three-dimensional treatment of [[combustion]] via [[direct numerical simulation]] at scales relevant for atmospheric modeling is not currently practical because of the excessive computational cost such a simulation would require. Numerical weather models have limited forecast skill at spatial resolutions under {{convert|1|km|mi|1|sp=us}}, forcing complex wildfire models to parameterize the fire in order to calculate how the winds will be modified locally by the wildfire, and to use those modified winds to determine the rate at which the fire will spread locally.<ref name="Clark-1996-CAFb">{{cite journal|author=Clark, T. L., M. A. Jenkins, J. Coen, and David Packham|title=A coupled atmospheric-fire model: Convective Froude number and dynamic fingering|journal=International Journal of Wildland Fire|volume=6|pages=177–190|year=1996|doi=10.1071/WF9960177|issue=4|url=https://zenodo.org/record/1236052}}</ref><ref name="Clark-1996-CAF">{{cite journal|author=Clark, Terry L., Marry Ann Jenkins, Janice Coen, and David Packham|title=A coupled atmospheric-fire model: Convective feedback on fire line dynamics|journal=Journal of Applied Meteorology|volume=35|pages=875–901|year=1996|doi=10.1175/1520-0450(1996)035<0875:ACAMCF>2.0.CO;2|bibcode=1996JApMe..35..875C|issue=6|doi-access=free}}</ref><ref name="Rothermel-1972-MMP">{{cite web |author=Rothermel, Richard C. |date=January 1972 |title=A mathematical model for predicting fire spread in wildland fires |url=http://www.fs.fed.us/rm/pubs_int/int_rp115.pdf |archive-url=https://web.archive.org/web/20220324005215/https://www.fs.fed.us/rm/pubs_int/int_rp115.pdf |archive-date=March 24, 2022 |access-date=2011-02-28 |publisher=[[United States Forest Service]]}}</ref>